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Migration, Remittances and the Brain Drain: Causes and Linkages1 Yoko Niimi and Çağlar Özden2 DECRG World Bank April 2007 Abstract The paper examines the determinants of remittance flows at the cross-country level. It considers, among other things, the significance of migration level, the education level of migrants and financial sector development in determining the amount of remittances received. The paper accounts for the potential endogeneity bias, or possible reverse causality between remittances and these key variables through instrumental variable estimation. Both the stock of migrants abroad and their education levels are found to be the main drivers of remittances. The results show a positive correlation between remittance flows and the level of migration, but surprisingly, remittances are negatively correlated with the education level of migrants. Key Words: migration, remittances, brain drain JEL Codes: F22, F24, J61, O15, O16 1 We would like to thank Maurice Schiff, L. Alan Winters, Pablo Fajnzylber, Caroline Freund, Humberto Lopez, Richard H. Adams and Michel Beine for comments and Ileana Cristina Neagu for her help with the data. The findings, interpretations and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank, its executive directors, or the countries they represent. 2 World Bank. Mailing address: DECRG, World Bank Mailstop MC3-303. 1818 H Street NW Washington, DC 20433. E-mail: [email protected], [email protected] 1 1. Introduction The recent years have witnessed a dramatic increase in migrant remittances to developing countries. Officially recorded remittances, measured as the sum of workers’ remittances, compensation of employees, and migrant transfers, are estimated to have increased from US$58 billion in 1995 to US$167 billion in 2005. This growth rate has outpaced that of private capital flows and official development assistance (ODA) over the last decade, making remittances the second largest source of external funding for developing countries after foreign direct investment (World Bank, 2005). There are several factors that are said to explain for the recent increase in remittance flows. They include growth in the number of migrants and their income, lower costs and wider networks in the global financial services industry, and government policies that improve financial market access, which promote the use of official channels (World Bank, 2005). Whatever the reasons behind this surge, the growing importance of remittances as a source of foreign exchange and their contribution to economic development have certainly attracted an increasing attention from policy-makers and academics. The existing empirical research on the determinants of remittances has, however, been rather limited and tended to be at the microeconomic level based on household surveys. Although there have been a number of studies that examine the response of remittance flows to macroeconomic factors (e.g. El-Sakka and McNabb, 1999; Aggarwal et al., 2005; Freund and Spatafora, 2005), the work in this area is less established. The main aim of this paper is improving our current understanding of how remittance flows are determined at the cross-country level. The main variables we consider are (1) the level of migration, (2) the education level of migrants and (3) the financial sector development of the home countries. One of the crucial issues in the migration literature is that migrants are not 2 selected from a random sample of the underlying native population of the home country. As a result, the desire to remit strongly influences migration levels and many other characteristics of migrants, such as their education levels. Thus the potential endogeneity biases surrounding the migration, remittances, brain drain and financial development linkages need to be properly taken into account in any empirical analysis. This is one of the main goals in the paper. Among all of the determinants of remittances, particular attention is paid to the effect of migrants’ education levels. With economic research providing increased evidence on the importance of human capital in the development process, the brain drain has become a very important area of concern. It is often argued that the negative effects of brain drain are offset by the fact that educated migrants typically earn more and thus remit more (e.g. Ratha, 2003). Some existing studies do not, however, support this view (e.g. Faini, 2006). It is an important question to interrogate further given that immigration policies of many destination countries are increasingly favoring relatively educated migrants from developed countries. Thus our paper provides interesting, and relatively negative, evidence to the debate on the impact of brain drain on economic development. The rest of the paper is structured as follows. The next section reviews the existing work on the determinants of remittances, identifies some of their limitations and describes how the present paper can contribute to the current literature. Section 3 provides a brief description of the data employed for the empirical analysis and specifies the estimation models. Section 4 presents the estimation results. The final section summarizes the findings. 3 2. Literature on Determinants of Remittances The existing literature on the determinants of remittances is largely based on microeconomic analyses. Lucas and Stark (1985), for instance, is a pioneering work on possible motives of migrant workers to remit home, based on the data drawn from the National Migration Study of Botswana. They consider several motivations to remit, ranging from pure altruism to pure selfinterest and find that tempered altruism or enlightened self-interest seems to explain the remittance behavior in their data. Tempered altruism/enlightened self-interest views remittance flows as part of a self-enforcing contractual arrangement between migrant and family, which are of mutual benefit. The migrant adheres to the arrangement as long as it is in his or her interest to do so, which may be either altruistic or more self-seeking (Lucas and Stark, 1985). Much of the existing work based on the micro data follows this approach (e.g. Aggarwal and Horowitz, 2002; Ilahi and Jafarey, 1998). A macro-level analysis of remittances is, on the other hand, less established. Aggregate remittance flows are likely to reflect the underlying microeconomic considerations that determine individual decisions to remit (El-Sakka and McNaab, 1999). As a result, some of the macroeconomic variables that have been examined include those concerning the economic conditions of home country as well as investment variables such as interest rate differentials between home and host countries (e.g. El-Sakka and McNabb, 1999; Higgins et al., 2004; Straubhaar, 1986; Swamy, 1981). Nonetheless, there is little consensus on the key macroeconomic determinants of remittance flows. Straubhaar (1986), for instance, finds that remittance flows from Germany to Turkey are not affected by exchange rate variations or by changes in the real rate of return on investment. On the other hand, using the data for Egypt, ElSakka and McNabb (1999) find that both exchange rate and interest rate differentials are important in attracting remittances through official channels. 4 Although country-specific work such as those noted here is useful in its own right, this limits us from generalizing the findings. There have been a number of studies that examine the macroeconomic determinants of remittances using cross-country or panel data sets in recent years. In a very innovative paper on informal remittance flows to developing countries, Freund and Spatafora (2005) estimate their determinants in order to predict their volume. They consider such variables as the cost of sending remittances through formal channels, the presence of exchangerate and other economic distortions, migration levels, and other country characteristics. They find that the stock of migrants in OECD countries is the primary determinant of remittances based on a panel data analysis. In addition, their results show the importance of financial services industry since that transaction costs are systematically related to concentration in the banking sector, lack of financial depth, and exchange rate volatility. Similarly, Buch and Kuckulenz (2004) investigate the determinants of remittances as well as of private and official capital flows using panel data. They find that traditional variables such as economic growth, the level of economic development, and proxies for the rate of return on financial assets do not have a clear impact on remittance flows. They argue that this may be because workers’ remittances share features of private and official capital flows, which are driven by different macroeconomic factors. These similarities are caused by the fact that remittances are to a large extent market-driven, but also influenced by social considerations of migrants. Yang (2005), on the other hand, examines the impact of hurricanes on various types of international financial flows to developing countries based on a panel data set. His results show that greater hurricane exposure leads to large increases in foreign aid. For other types of international financial flows, he finds the varying impact of hurricanes according to the income 5 level of the country. In the poorer half of the sample, for instance, hurricane exposure is found to generate a substantial increase in migrants’ remittance flows. These studies have certainly provided us with a broader view of how remittance flows are determined. However, there is sill a great scope for improving our current understanding of the issue. One of the factors that deserve more attention is the impact of the education level of migrant workers on remittances at the cross-country level. If a significant proportion of migrants are highly educated, they are likely to earn higher income and thus remit more. Yet it can also be argued that educated migrants tend to come from better-off families who rely less on remittances for their livelihood. It is therefore not clear as to which force dominates (Niimi and Ozden, 2006). In his analysis of the skill level of migrants on remittance flows based on a cross-country panel data set, Faini (2006) finds no evidence that skilled migrants remit more. He argues that this is not only because skilled migrants tend to come from relatively wealthy families, but also because they have a lower propensity to remit home, presumably reflecting the fact that they are keener (and more able) to bring their close family members in the destination country (Faini, 2006). His findings indeed question the common belief that the negative effects of brain drain are offset by the fact that educated migrants typically earn more and thus remit more. Faini’s findings certainly have an important policy implication given that immigration policies are increasingly favoring relatively educated migrants in most developed destination countries. Nonetheless, his study is not without any caveat. Most importantly, there is a potential endogeneity problem between remittance flows and the education level of migrants. Some of the existing studies, for example, show the positive impact of remittances on school attainment in home countries (e.g. Cox-Edwards and Ureta, 2003; Duryea et al., 2005). This suggests a possible reverse causality between remittances and migrants’ education level. It is therefore important to 6 account for the possible endogeneity bias in the analysis which is one of our main goals in this paper. Another important determinant we should examine is financial sector development of home countries. While several studies have examined investment or portfolio variables as noted above, there is little work that assesses the effect of financial development on remittance flows at the macroeconomic level with several exceptions. Based on a panel data analysis, Aggarwal et al. (2005) show the positive effects of remittances on the development of financial sector measured as the ratio of bank deposit or credit to GDP. They, however, also recognize the possibility that better financial development leads to larger measured remittances either because financial development enables remittance flows or a larger percentage of remittances is measured when those remittances flow through formal channels. Additionally, financial development might lower the transaction costs, which in turn increase remittances. This potential reverse causality is addressed through instrumental variable (IV) and Arellano-Bond Generalized Method of Moments (GMM) estimations. It would thus be interesting to test the significance of financial sector development as a determinant of remittances. Freund and Spatafora (2005) do include the financial development variable in their panel regression of the determinants of remittances and find its positive and significant effect on remittance flows. However, they do not control for any reverse causality between financial sector development and remittance levels. In sum, this paper aims to contribute to the existing literature by examining the impact on remittance flows of, among other things, the following three factors: (1) the level of migration; (2) the education level of migrants; and (3) financial sector development of home countries. The endogeneity issues regarding these three variables discussed earlier will be taken into account in our analysis through the IV estimation. 7 3. Econometric Specification and Data Table 1 summarizes the mean values of some of the key variables by regions in order to provide an overview of their variation across regions. The ratio of measured remittances to GDP is found to be the highest in the Middle Eastern and North African countries, followed by the Sub-Saharan countries. On the other hand, in terms of its ratio of migrants to the population, Western European countries have the highest level, mainly due to intra-European labor flows, and they are followed by the American and the Middle Eastern & North African countries. As for the education level of migrants, it is interesting to observe that more than half of the migrants from both the SubSaharan Africa and South Asia have tertiary education. These figures can be compared with the ratio of people with tertiary education to the total population in home countries. The comparison illustrates that migrants are significantly more highly educated than the rest of the population across the world. However, the difference is substantial, particularly for the Sub-Saharan Africa and South Asia. This seems to underline the brain drain issue, which is a growing concern in many developing countries (see e.g. Ozden and Schiff, 2006). [Table 1 around here] The table has shown some important variations in remittance flows as well as in other factors including the number of migrants and their education level across the regions. These variations would certainly be even greater if we were to examine across the countries. The main aim of our empirical analysis is to examine what are the key determinants of these observed variations in remittance flows. 8 3.1 Econometric Specification In order to empirically examine the significance of various factors as the determinants of remittance flows, the following equation will be estimated: (1) REM i i1 MIGi i2 FDi i3GDPi i4 GDPgrowthi i5 Edu i i where i refers to home country i. For our dependent variable, REMi, we will try three different variables, namely the ratio of remittances to GDP, the logarithm of GDP, and the logarithm of remittances in per-capita terms. MIGi is the level of migration from country i. We express this either as the logarithm of migrants abroad or as the ratio to population size of home country i. FDi is financial sector development variable of country i, which is the ratio of bank deposit to GDP. As for GDPi, both the logarithm of GDP and of GDP per capita are employed. GDPgrowthi is the growth rate of GDP of country i and Edui is the ratio of migrants with tertiary education to the total number of migrants. αi and βi are parameters to be estimated and εi is an error term. Table 2 summarizes all the variables employed for the estimation. We start by estimating equation (1) by Ordinary Least Squares (OLS) by ignoring any possible endogeneity problems. We will then estimate it through the IV estimation, first only instrumenting the migration variables, then instrumenting the migration and education variables, and finally the migration, education and financial development variables. The rest of the section describes the variables and discusses the endogeneity issues and our choice of instruments for the IV estimation. [Table 2 around here] 9 Stock of migrants abroad The level of migration is likely to be the most important determinant of the size of remittances. This is expressed in two different specifications - the logarithm of the number of migrants abroad and it’s the ratio of migrants to population in home countries. The key issue that needs to be properly addressed is the endogeneity bias since the desire to send remittances is among the main reasons behind the migration decision of most people. We control for the endogeneity bias through the IV estimation. The labor market participation rate, the population density, the percentage of urban population, the dependency ratio (i.e., the ratio of dependents to working-age population), the distance between the origin and destination countries expressed in terms of the great-circle distance, an English language dummy variable that is unity if English is spoken in home country, and the cost of obtaining a national passport as a share of GDP per capita are all employed as identifying instruments for the level of migration. The qualifying requirement for being an appropriate instrument is that they should be correlated with the regressor for which it is to serve as an instrument, but uncorrelated with the dependent variable. In other words, they should be correlated with the number of migrants abroad, but not with remittance flows. The first four variables represent the demographic/labor market conditions of home countries whereas the latter three variables indicate the costs of migrating to destination countries. The labor market participation rate and urbanization rate (assuming there are more working opportunities in urban areas) are likely to indicate the availability of work in home countries, which will be negatively correlated with the number of migrants leaving their home country. On the other hand, population density in home countries is often thought to be a push factor for migration. As for the dependency ratio, its lower figure informs us a higher percentage of 10 working-age population. Hence this variable will be negatively correlated with the level of migration. These four variables are thought to represent the demographic trends of home countries and we believe that remittance flows are unlikely to influence these long-term trends. As far as the costs of migrating are concerned, the longer the distance between the home and destination countries and the higher the passport cost, the greater the cost of migrating and thus the lower the number of migrants abroad. In addition, if English is commonly spoken in home country, this reduces the migration cost and, as a result, generates a greater outflow of migrants. The available empirical evidence shows the importance of distance in determining the level of migration (e.g. Mayda, 2006) Moreover, McKenzie (2005) exploits the observed variation in the cost of obtaining a national passport across the world and finds high passport costs are associated with lower levels of outward migration. It should be reasonable to assume these variables are not correlated with remittances and we therefore use them to instrument for the stock of migrants. Given that there is no theoretical guidance as to which variables should be used as the identifying variables, it cannot be denied that our verification of the set of identifying variables is a crude way of determining their validity. This applies to the other sets of instruments described below and should be taken into account when discussing the results. Education level of migrants Our education variable is the ratio of migrants with tertiary education to the total number of migrants. The expected sign of its effect is unclear as there are some countervailing forces. On the one hand, a higher level of education among migrants enables migrants to earn more and thus remit more. On the other hand, it implies that these migrants are likely to come from better-off households relative to the rest of the population and thus there is less need to send money to their 11 families at home. In addition, it is easier for educated migrants to obtain legal residency in the destination country and bring their immediate families over with them, which would decrease the incentives to remit money back home (Niimi and Ozden, 2006). As discussed earlier, we have to control for the possible endogeneity bias between remittances and migrants’ education level. The instruments used in our IV estimation are the logarithm of public spending on education and per student tertiary education expenditure. They are employed in turn. These variables are expected to positively affect the education level of migrants, but we can reasonably assume that remittance flows are unlikely to affect the size of these public expenditures. Financial Sector Development The more developed and efficient the financial sector of home countries is, the higher will be amount of remittances sent by migrants and the higher will be share sent through formal channels. A developed financial market lowers the transaction costs, increases the accessibility of recipients to the money sent and provides investment opportunities for the savings of migrants. Hence we will assess whether financial sector development has any positive impact on remittance flows using the ratio of the bank deposit to GDP. Recall, however, that the recent work by Aggarwal et al. (2005) shows that the level of remittances also has a positive impact on the development of the financial sector of home countries. We therefore control for the possible endogeneity bias by instrumenting the financial development variable by inflation rate and a dummy variable for controlling for countries with British legal origin (i.e., whether their legal system is based on Common Law) as they are suggested in Aggarwal et al. (2005) as the determinants of financial sector development. It has been shown that there is a significant and economically important negative relationship between inflation and both banking sector development and equity market activity (Boyd et al., 2001). Further, given that the existing literature provides enough evidence for the importance of legal institutions for financial sector development (e.g., Beck et al., 2003; 12 La Porta et al., 1997, 1998), a dummy for countries with British legal origin (i.e., it is unity if the country’s legal system is based on Common Law) is also employed as an instrument. Although the latter is unlikely to be correlated with remittance flows, we may not be able to entirely rule out the possibility of correlation between remittances and inflation. This should be taken into account when interpreting the results. Economic conditions of home countries As a proxy for the general state of the development and size of home countries’ economy, we will also include the logarithm of GDP per capita (ppp adjusted) and of total GDP. The former measures the level of economic development of home countries and the expected sign of the coefficient would be negative because migrants from wealthier countries are likely to have less incentive to remit. As for the logarithm of GDP, which captures the size of home countries’ economy, we would expect the sign to depend on how we express our dependent variables, i.e., remittance variables. For example, if the dependent variable is the natural logarithm of remittances, then this should increase with the size of the country since larger countries, such as China or India, receive more in absolute amount. If the dependent variable is expressed as a share of GDP, we would expect this to decrease with the country size. There is much evidence that argues smaller and isolated small economies are less likely to sustain many economic activities, which induces their citizens to migrate for better opportunities. This would in turn increase remittance flows to these economies. In addition to the above variables, the rate of economic growth of home countries is included in the estimation equation. Its expected impact on remittance flows is not very clear. It can be argued that the growing economy reduces the incentives to migrate and hence implies less remittance to those countries. It is, however, equally possible that the high economic growth rate might induce migrants to invest more at home and thus increase remittance inflows to home countries. 13 3.2 Data The data used for our analysis covers 85 countries, for which we have observations for all the variables employed in the estimation. Although it would be ideal to have a panel data set, we will concentrate our analysis on a single point in time, 2000, given that there are no time-series data on the number of migrants abroad and their education level. Aggregate data on remittances come from the IMF’s Balance of Payment statistics, which are the sum of the three items, namely workers’ remittances, compensation of employees, and migrant transfers. The number of migrants in OECD countries and the ratio of those with tertiary education among migrants come from Docquier and Marfouk (2006). The cost of acquiring a national passport as a percentage of GDP per capita is obtained from McKenzie (2005). Our financial sector development variable, namely the ratio of bank deposit to GDP, is from the IMS’s International Financial Statistics. The rest of the variables mostly come from the World Development Indicators. Appendix I provides a description of the variables and their sources in more details. 4. Estimation Results 4.1 OLS Estimation Our estimation starts with OLS whereby we ignore the endogeneity issues discussed in the previous section. We estimate three regressions using a different definition of the dependent variable, namely the ratio of remittances to GDP, the logarithm of remittances per capita, and the logarithm of remittances. Each variable represents a different dimension of the economic importance of remittances. Remittance to GDP ratio represents overall size of the remittances relative to the economy. Remittances per capita capture the importance at the individual level. 14 These are related but distinct measures of remittances. For example, for two countries with similar remittances/GDP ratio, a poorer country will have a higher remittances per capita level. We use migrants to population ratio as the main explanatory variable in the specifications that employ these two remittance variables. On the other hand, we simply use logarithm of migrants as the explanatory variable in the specification with logarithm of remittances. The first three columns of Table 3 report the results. The level of migration is positively correlated with the amount of remittances sent home as we would expect, though it is significant only in two specifications (columns 2 and 3). For instance, one percent increase in the number of migrants increases remittance flows by about 0.4 percent (column 3). However, given that the increase is less than one percent (i.e., the coefficient is less than the unity), an increase in the level of migration actually decreases remittances sent by each migrant! This may be partly explained by family reunification effect. It would be interesting to examine such an effect, but unfortunately our data do not allow us to do so. [Table 3 around here] As far as the education level of migrants is concerned, its effect on remittances is found to be negative, though significant only in one specification (column 2). The negative sign of the coefficients implies that relatively educated migrants are likely to remit a smaller amount of remittances to their home countries. These results are consistent with Faini (2006). This could be due to the fact that educated migrants come from relatively better-off families and there is less need to send money home to support the livelihood of their family members. We could also argue that migrants with higher level of education are more capable of bringing their close family members to their destination countries, and thus there is less need to send money home. The impact of the financial sector development of home countries is positive, but not significant in any of the specifications. The effect of the size of the domestic economy and the level of 15 economic development of home countries varies depending on the definition of the dependent variable. While smaller countries receive a relatively small amount of remittances, they receive more as a percentage of GDP, which is intuitive. The smaller domestic economy presumably has relatively limited opportunities for economic activities, and migrants thus have to remit more to support their families at home. Moreover, migrants from poorer countries are found to remit a greater amount of money home in terms of the absolute amount or a share of GDP. However, poorer countries seem to receive less remittances in per-capita terms. As for the economic condition of home countries, the economic growth does not seem to have any effect on remittance flows. This is consistent with some of the existing literature (e.g. Buch and Kuckulenz, 2004). This is likely to be due to the fact that the two countervailing forces described above – high economic growth may reduce the incentives to migrate and hence lead to less remittances, but it can also induce migrants to invest more at home – cancel out. 4.2 IV Estimation (1) We move on to the IV estimation that enables us to control for the possible endogeneity problems. We first instrument only for the migration variables (IV (1)). The results are reported in Table 4. In order to check the validity of our set of instruments, the Hansen J-statistics for the overidentification test are reported at the bottom of Table 4. The test results support the validity of our instruments, i.e., they are uncorrelated with the error term and excluded instruments are correctly excluded from the estimated equation. Furthermore, our instruments are found to explain a good deal of variation in the variables that they are instrumenting. The validity of our instruments is supported by our results also for the other specifications where we instrument not only for the migration level, but also for the education and financial development variables. [Table 4 around here] 16 The most striking difference in the results between the OLS and IV estimation is found in the coefficients on the migration variables. When we control for the endogeneity of the migration variables, the coefficients become statistically significant in all three specifications and significantly larger in the absolute size. Based on this set of results, for example, one percent increase in the number of migrants abroad will increase remittances by about 0.7 percent (column 3 of Table 4) in comparison with 0.4 percent based on the OLS results (column 3 of Table 3). This difference endorses our claim for the importance of controlling for the endogeneity bias. The rest of the results are relatively similar to those from the OLS estimation. Both the education and financial development variables are not statistically significant even though we control for the endogeneity of the migration variables. It should briefly be noted that most of the coefficients on our instruments have the expected sign. The key determinant of the stock of migrants seems to be whether English is commonly spoken in the home country. If English is widely spoken in a particular country, the people from the country are more likely to migrate, presumably because the ability to speak English eases the language barrier when migrating to a foreign country. 4.3 IV Estimation (2) We now instrument for both the migration and the education variables. In addition to the instruments employed for the stock of migrants, variables for public expenditures on education are also included as our instruments. These expenditure variables are found to be positively correlated with the ratio of migrants with tertiary education as we would expect. Among the other instruments, the urbanization rate, the distance between home and destination countries and the language variable have the significant coefficients. It is interesting to observe the positive correlation between the education level of migrants and the distance variable while we observe 17 the negative correlation between the overall stock of migrants and the distance. These findings suggest that educated migrants migrate to a country that is relatively far from their home country, presumably because they are more capable of affording the cost of migrating to longer-distant countries. Table 5 reports the second-stage regression results for IV (2). The negative effect of the education level of migrants is found to be statistically significant in all three specifications once we control for the endogeneity of both the migration and education variables. In his analysis of the determinants of remittances, Faini (2006) also finds a negative correlation between remittances and the education level of migrants. However, his coefficient on his education variable is not statistically significant even when he accounts for the endogeneity bias in the total migration level. Since the coefficients only became significant when we controlled for both the migration and education variables in our analysis, the statistically insignificant effects of migrants’ education level found in Faini’s estimation might be explained by the endogeneity bias. According to our estimation results, a one-percent increase in the number of migrants will lead to a decrease of about 0.3 percent in remittance flows in terms of its ratio to GDP (column 1 of Table 5). Our results therefore do not seem to support the view that the negative impact of brain drain on home countries is mitigated by the fact that educated migrants remit more to their families. [Table 5 around here] As far as the migration variables are concerned, we find the positive and statistically significant correlation between remittance flows and migration level, though the absolute size of their effects are smaller than those estimated in IV (1). The coefficient on the financial development variable has the expected sign, but again not statistically significant. The significance and sign of the other coefficients are generally consistent with the previous two specifications (i.e., with OLS and IV (1)). 18 4.4 IV Estimation (3) Finally, we instrument for the migration, education and financial development variables. As our explanatory variables for the financial development variable, we included inflation rate and a dummy for the British legal origin in the first-stage regression as in Aggarwal et. al. (2005). Despite the fact that we control for the endogeneity of the financial development variable, its coefficient is not statistically significant in any specification (Table 6). Hence although we find the positive impact of the financial sector development of home countries on the amount of remittances sent home, its significance is not supported by our analysis. This certainly merits further investigation, particularly on the choice of other possible instruments. [Table 6 around here] In sum, the stock of migrants abroad, the education level of migrants and the level of economic development of home countries are found to be the key determinants of international remittance flows. The greater the number of migrants abroad, the larger the amount of remittances sent home as we would expect. In addition, we have also found that less educated migrants are more likely to remit home, perhaps due to the fact that they tend to come from less well-off families and thus need to send money to support the livelihood of their family members, or because they are not capable of bringing their immediate family members to the destination country. Furthermore, our findings suggest that poorer countries are likely to receive relatively more remittances in terms of the absolute total amount and its ratio to GDP, though they are found to receive less in per-capita terms. 5. Conclusion The paper has examined empirically the significance of some of the key factors in determining remittance flows based on the cross-country data. The results show that the level of migration and 19 the education level of migrants are the main drivers of remittances. The size of domestic economy and the level of economic development of home countries are also found to play a role in determining the flow of remittances. Financial sector development seems to have a positive impact on remittances, but its significance is found to be less clear. Our findings also illustrate the importance of accounting for the potential endogeneity of the stock and education levels of migrants in the estimation. Some important policy implications can be drawn from these findings. Firstly, the negative correlation between remittance flows and migrants’ education level suggests that less educated migrants who are more likely to come from less well-off families are remitting more to their families at home. Moreover, poorer countries are found to be receiving relatively more remittances. These findings seem to suggest some possible positive impact of remittances on the welfare of recipient families and of home countries, though a more detailed microeconomic analysis is obviously required to make any definitive conclusion. However, the common belief that the negative effect of brain drain is mitigated by remittances sent by those educated migrants is not supported by our results. Further analysis is obviously required to examine the robustness of the findings presented in this paper. Despite the limited availability of data, a panel data analysis certainly merits future work. Moreover, our empirical estimation was conducted based on the officially measured remittance data. We are therefore not able to generalize our findings on the determinants of remittance flows to the case of remittances transferred through informal channels. Because of their particular relevance to the economic development of developing countries, more empirical work on informal remittance flows is certainly required. This in turn indicates the need for further efforts in collecting data on migration and remittances. 20 Reference Aggarwal, R., A. Demirguc-Kunt, and M. S. Martinez Peria (2005) “Do Workers’ Remittances Promote Financial Development?”, mimeo, Washington, D.C.: World Bank. Aggarwal, R. and A. Horowitz (2002) “Are International Remittances Altruism or Insurance?: Evidence from Guyana Using Multiple-Migrant Households”, World Development, Vol.30, pp.2033-2044. Beck, T., A. Demirguc-Kunt, and R. Levine (2003) “Law, Endowments, and Finance”, Journal of Financial Economics, Vol.70, pp.137-181. Boyd, J. H., R. Levine and B. D. Smith (2001) “The Impact of Inflation on Financial Sector Performance”, Journal of Monetary Economics, Vol.47, pp.221-248. Buch, C. M. and A. 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(2005) “Coping with Disaster: The Impact of Hurricanes on International Financial Flows, 1970-2002”, mimeo, University of Michigan. 22 List of Tables: Table 1: Summary Statistics (mean values) by Regions (sample size: 85 countries) Rem/GDP (%) Americas Western Europe Eastern and Central Europe Middle East and North Africa Sub-Saharan Africa South Asia East Asia and Pacific 1.44 0.48 0.88 3.38 2.95 2.47 0.58 Mig/Pop (%) Ratio of migrants with tertiary edu. (%) 2.41 3.60 1.55 2.09 0.39 0.21 0.36 37.4 34.3 45.0 30.3 55.4 55.3 49.9 Ratio of population with tertiary edu. in home countries (%) 11.9 18.6 17.4 9.4 3.5 4.4 6.6 Bank deposit/GDP (%) 25.1 74.9 25.2 53.2 22.2 39.6 102.0 GDP per capita, ppp (2000 constant, international $) 7,275 24,550 7,798 5,396 2,282 2,203 5,827 1.45 0.92 48.2 8.0 64.1 6,382 Sample average Note: Figures are weighted by the population. They are based on the mean values for the period between 1998-2002 except for the number of migrants abroad, the ratio of migrants to population, and the ratio of migrants with tertiary education to total migrants, which are the figures for the year 2000. 23 Table 2: List of Dependent and Explanatory Variables Mean Dependent variables Ratio of remittances to GDP (%) Log of remittances Log of remittances per capita Independent variables Log of migrants abroad Ratio of migrants abroad to population size Ratio of bank deposit to GDP Log of GDP Log of GDP per capita, ppp GDP growth rate (%) Ratio of migrants with tertiary education to total number of migrants (%) S.D. 2.839 19.489 3.263 4.601 1.985 1.586 12.188 4.902 0.475 24.171 8.746 3.475 1.620 7.587 0.299 2.229 1.085 2.221 39.225 13.972 Instrumental variables Labor market participation rate (%) 68.850 7.698 Population density (people per sq km) 140.548 191.139 Urbanization (%) 55.868 22.979 Dependency ratio 0.629 0.163 Log of distance (km) 1.171 0.950 Dummy for English language 0.318 Passport cost (% of GDP per capita) 3.003 5.698 Log of public spending on education, ppp 21.717 2.042 Per student tertiary education expenditure, ppp 4,324 3,525 Inflation rate (%) 8.109 13.723 Dummy for British legal origin 0.329 Note: All the variables are the mean values for the period between 1998-2002 except for the logarithm of migrants abroad, the ratio of migrants to population, and the ratio of migrants with tertiary education which are the figures for the year 2000, and for public spending on education, the logarithm of public spending on education, school enrollment and per student tertiary education expenditure that are the mean values for the period between 1990-2000. They are unweighted means. 24 Table 3: Regression Results: OLS Rem/GDP Migrants/population 0.134 [0.133] OLS Log of Rem per capita 0.066*** [0.020] Log of migrants Ratio of migratns with tertiary edu. Bank deposit/GDP Log of GDP Log of GDP per capita GDP growth Constant -0.040 [0.027] 0.925 [1.373] -0.416 [0.506] -1.216*** [0.438] -0.261 [0.336] 24.896** [10.813] Log of Rem -0.022** [0.010] 0.366 [0.861] 0.493*** [0.192] -0.051 [0.078] -0.503 [1.387] 0.390** [0.179] -0.012 [0.012] 0.655 [0.670] 0.556*** [0.117] -0.521** [0.215] -0.058 [0.074] 6.206*** [1.904] 85 85 85 Observations 0.268 0.363 0.628 R2 5.13 (0.00) 9.73 (0.00) 18.34 (0.00) F-statistic (p-value) Note: Figures in parentheses are robust standard errors. ***significant at 1% level, **significant at 5% level, and *significant at 10% level. 25 Table 4: Regression Results: IV (1) Instrumenting only for the migration variable Rem/GDP Migrants/population 0.337* [0.176] IV (1) Log of Rem per capita 0.112** [0.045] Log of migrants Ratio of migratns with tertiary edu. Bank deposit/GDP Log of GDP Log of GDP per capita GDP growth Constant -0.027 [0.034] -0.482 [1.367] 0.051 [0.280] -1.724*** [0.432] -0.205 [0.310] 17.045*** [6.584] Log of Rem -0.016 [0.011] 0.132 [0.856] 0.493*** [0.182] -0.047 [0.076] -0.861 [1.366] 0.710*** [0.230] 2.97E-04 [0.013] 0.486 [0.634] 0.362** [0.180] -0.429** [0.220] -0.062 [0.078] 5.810*** [1.952] 85 85 85 Observations 0.209 0.326 0.601 R2 4.69 (0.00) 9.39 (0.00) 19.46 (0.00) F-statistic (p-value) 8.57 (0.20) 8.92 (0.18) 6.39 (0.38) Overidentification χ2 (p-value) Note: Figures in parentheses are robust standard errors. ***significant at 1% level, **significant at 5% level, and *significant at 10% level. 26 Table 5: Regression Results: IV (2) Instrumenting for the migration and education variables Rem/GDP Migrants/population 0.282** [0.123] IV (2) Log of Rem per capita 0.086*** [0.033] Log of migrants Ratio of migratns with tertiary edu. Bank deposit/GDP Log of GDP Log of GDP per capita GDP growth Constant -0.098** [0.044] 0.465 [1.258] 0.053 [0.393] -1.948*** [0.499] -0.295 [0.337] 21.875** [9.154] Log of Rem -0.043** [0.018] 0.515 [0.812] 0.410** [0.180] -0.081 [0.079] 1.005 [1.526] 0.444** [0.209] -0.031* [0.018] 0.804 [0.636] 0.564*** [0.138] -0.618*** [0.211] -0.082 [0.077] 6.984*** [2.024] 85 85 85 Observations 0.195 0.313 0.607 R2 3.75 (0.00) 8.77 (0.00) 17.85 (0.00) F-statistic (p-value) 7.37 (0.29) 8.75 (0.19) 6.40 (0.38) Overidentification χ2 (p-value) Note: Figures in parentheses are robust standard errors. ***significant at 1% level, **significant at 5% level, and *significant at 10% level. 27 Table 6: Regression Results: IV (3) Instrumenting for the migration, education and financial development variables Rem/GDP Migrants/population 0.233* [0.125] IV (3) Log of Rem per capita 0.088*** [0.033] -0.092** [0.042] 1.779 [3.233] -0.098 [0.449] -1.930*** [0.552] -0.308 [0.349] 24.808** [11.659] -0.042** [0.018] 0.333 [1.172] 0.441** [0.222] -0.079 [0.081] 0.760 [1.938] 0.429** [0.201] -0.033* [0.020] 1.565 [1.193] 0.561*** [0.146] -0.738*** [0.258] -0.090 [0.086] 8.040*** [2.698] 85 0.220 3.76 (0.00) 8.06 (0.33) 85 0.315 8.09 (0.00) 8.74 (0.27) 85 0.597 18.35 (0.00) 6.61 (0.47) Log of migrants Ratio of migratns with tertiary edu. Bank deposit/GDP Log of GDP Log of GDP per capita GDP growth Constant Observations R2 F-statistic (p-value) Overidentification χ2 (p-value) Log of Rem 28 Appendix: Variable Definitions and Sources Variable name Description Source Remittances to GDP Ratio of remittances to GDP (%), where remittances are defined as the sum of “workers’ remittances”, “compensation of employees” , and “migrants’ transfers” (see Appendix A in Freund and Spatafora (2005)). Log of remittances (constant 2000 US$), which are calculated by multiplying the ratio of remittances to GDP by GDP figures. Balance of Payments Statistics (IMF) Log of remittances Log of remittances per capita Log of remittances per capita (constant 2000 US$). Log of migrants abroad Log of total number of migrants in OECD countries. Ratio of migrants to population Ratio of total number of migrants in OECD countries to the population size of home countries (%). Ratio of migrants with tertiary education to the total number of migrants Bank deposit to GDP Ratio of migrants with tertiary education to the total number of migrants (%). Log of GDP Bank deposit to GDP calculated as: {(0.5)*[F(t)/P_e(t) + F(t-1)/P_e(t-1)]}/[GDP(t)/P_a(t)] where F is demand and time and saving deposits, and the rest as defined as above. Log of GDP (constant 2000 US$). Log of GDP per capita Log of GDP per capita, ppp adjusted (constant 2000 international $). GDP growth GDP growth (annual %). Labor market participation rate Labor market participation rate (%) – percentage of population aged 15-64 who are in labor market. Population density Population density (people per sq km). Urbanization Urban population (%) – percentage of population living 29 Remittances: Balance of Payments Statistics (IMF) GDP: World Development Indicators Remittances: Balance of Payments Statistics (IMF) Population: World Development Indicators Docquier and Marfouk (2006) Migrants: Docquier and Marfouk (2006), Population: World Development Indicators Docquier and Marfouk (2006) International Financial Statistics (IMF) World Development Indicators World Development Indicators World Development Indicators World Development Indicators World Development Indicators World in the urban sector. Dependency ratio Age dependency ratio – ratio of dependents to working-age (15-64) population. Log of distance Log of distance between destination and home countries calculated using the formula for the greatcircle distance (km). We calculated for each region as follows: USA, Canada, EU, Australia, New Zealand: Zero distance Eastern and Central Europe, Middle East, Africa: Average distance to EU countries weighted by the number of migrants Central America, Mexico, Caribbean, South America: Distance to USA South Asia, East Asia and Pacific: Average distance to USA/Canada and EU countries weighted by the number of migrants. English language (1 indicates countries where English is commonly spoken). English language dummy Passport cost to GDP per capita Inflation Passport cost normalized by the country’s GDP per capita expressed in US dollars, inflation adjusted (%): (passport cost/ GDP current dollars per capita)*100/((1+inflation90/100)(1+inflation91/100)… .((1+inflation2004/100)) Inflation, consumer prices (annual %). British legal origin British legal origin (1 indicates countries with Common Law origin). Log of public spending on education Log of public spending on education, ppp adjusted (constant 2000 international $). Per student tertiary education expenditure Per student tertiary education expenditure, ppp adjusted (constant 2000 international $). 30 Development Indicators World Development Indicators Authors’ calculations based on data from CIA World Factbook Docquier (2006) and CIA World Factbook Passport cost: McKenzie (2005) GDP: World Development Indicators World Development Indicators La Porta, López de Silanes, Shleifer and Vishuny (1998) World Development Indicators World Development Indicators